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Figure3_imputation.Rmd
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Figure3_imputation.Rmd
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---
title: "Figure3_imputation"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# libraries
```{r message=FALSE}
library(plyr)
library(dplyr)
library(tidyr)
library(tibble)
library(ggplot2)
library(ComplexHeatmap)
set.seed(4242)
library(ggcorrplot)
library(corrplot)
library(circlize)
library(RColorBrewer)
library(viridis)
library(readxl)
library(colorspace)
```
# Figure 3.A
```{r}
classic_criteria_dil <- read_excel("./main_fig_data/classic_criteria_dil_80%.xlsx")
classic_criteria_dil <- column_to_rownames(classic_criteria_dil, "Methods")
rownames(classic_criteria_dil) <- c("BPCA", "LLS", "Random Forest", "KNNmethod",
"SVDmethod", "minDet", "minProb","Zero")
SOR_NRMSE_dil <- subset(classic_criteria_dil, select = c(SOR))
classic_criteria_dil <- subset(classic_criteria_dil, select = -c(SOR))
PSS_dil <- subset(classic_criteria_dil, select = c(PSS))
classic_criteria_dil <- subset(classic_criteria_dil, select = -c(PSS))
colnames(classic_criteria_dil) <- c("NRMSE", "ACC")
colAnn2 <- rowAnnotation(`SOR_NRMSE` = anno_barplot(SOR_NRMSE_dil$SOR),
width = unit(2, "cm"))
colAnn3 <- rowAnnotation(`PSS` = anno_barplot(PSS_dil$PSS),
width = unit(2, "cm"))
s9 <- sequential_hcl(9, "Reds", rev = T)
hmap <- Heatmap(
classic_criteria_dil,
name = "Classic criteria",
show_row_names = T,
show_column_names = T,
cluster_rows = F,
cluster_columns = F,
show_column_dend = TRUE,
show_row_dend = FALSE,
row_dend_reorder = TRUE,
column_dend_reorder = TRUE,
clustering_method_rows = "ward.D2",
clustering_method_columns = "ward.D2",
width = unit(100, "mm"), right_annotation = colAnn2,
left_annotation = colAnn3,
col = s9,
column_names_gp = gpar(fontsize = 25),
row_names_gp = gpar(fontsize = 25),
heatmap_legend_param = list(
title = "Classic criteria",
title_position = "leftcenter-rot"))
draw(hmap, heatmap_legend_side="left")
```
# Figure 3.B
```{r}
prot_criteria_dil <- read_excel("./main_fig_data/prot_criteria_dil_80%.xlsx")
prot_criteria_dil <- column_to_rownames(prot_criteria_dil, "Methods")
rownames(prot_criteria_dil) <- c("BPCA", "KNNmethod", "LLS", "Random Forest",
"SVDmethod", "minDet", "minProb", "zero")
hmap <- Heatmap(
prot_criteria_dil,
name = "Proteomic criteria",
show_row_names = T,
show_column_names = T,
cluster_rows = F,
cluster_columns = F,
show_column_dend = TRUE,
show_row_dend = FALSE,
row_dend_reorder = TRUE,
column_dend_reorder = TRUE,
clustering_method_rows = "ward.D2",
clustering_method_columns = "ward.D2",
width = unit(100, "mm"),
col = s9,
column_names_gp = gpar(fontsize = 25),
row_names_gp = gpar(fontsize = 25),
heatmap_legend_param = list(
title = "Proteomic criteria",
title_position = "leftcenter-rot"))
draw(hmap, heatmap_legend_side="left", annotation_legend_side="right")
```